Wireless communication systems modulate data onto electromagnetic carriers, and transmit the data from one or more transmit antennas, across an air interface, to one or more receiver antennas. Processing circuits and software at the receiver then attempt to recover the data from the received signal, which includes the data, interference, and noise. By estimating and suppressing interference and noise, the data may be recovered more accurately. Interference cancellation is thus a ubiquitous feature of wireless communication systems radio receivers, both in fixed network transmission sites (variously known as base stations, Node B, or Access Points) and mobile User Equipment (UE, also known as mobile stations). To accurately estimate (and hence cancel) interference, estimates of the channel must first be formulated which is well modeled as a time varying FIR filter with L+1 taps. Channel estimation in an interference cancellation receiver is a challenging task due to possibly very low operating carrier to interference ratio (C/I).
Two known approaches to channel estimation (both explored more fully herein) are correlation based channel estimation, and least squares based channel estimation. Correlation channel estimation correlates a known training sequence in the received signal to its known values, multiplying with the complex conjugate at different offsets and the correlation peak values are the channel estimates. Least squares channel estimation minimizes the sum of the squared error quantities of the difference between the received signal and the predicted signal which is the known training sequence symbols passing through the FIR filter.
A channel estimator can be characterized as either biased or unbiased. An estimator is biased if its statistical expected value is not equal to the true value being estimated. The estimator is unbiased if its expected value is the true value being estimated. In addition to its mathematical structure, whether an estimator is biased or unbiased also depends on the statistics of channel impairment, which includes white noise and interference. An impairment is referred to as “white” if it has a substantially uniform spectral power density—that is, it exhibits a flat frequency spectrum, with equal power in any bandwidth. Conversely, impairment is referred to as “colored” if it has a non-uniform spectral power density. If impairment is white, the least squares channel estimate is unbiased whereas the correlation channel estimate is biased.
To cancel interference, a receiver must also estimate a spatial-temporal whitening filter. There are two different approaches. In Spatial-Temporal Interference Rejection Combining (ST-IRC) and indirect Generalized Least Squares (iGLS), the radio channel and the whitening filter are estimated jointly. Alternatively, in iterative channel estimation, the channel is estimated first, and the channel estimates are then used to estimate the whitening filter.
Joint channel estimation has better performance than iterative channel estimation since interference cancellation is part of the channel estimation. The drawback is the high computational complexity. In joint estimation, many parameters are estimated simultaneously, which requires the inversion of large matrices. This is computationally difficult to implement using available processors, such as 16-bit fixed-point DSP devices.
In contrast, iterative channel estimation is much less computationally demanding, and hence can more easily and inexpensively be implemented. In iterative channel estimation, only small matrices need to be inverted, e.g., a 2×2 compared to a 13×13 matrix for iGLS. However, since the initial channel estimation is done without interference cancellation, the receiver must re-estimate the channel after the interference cancellation or whitening.
Due to the low complexity and numerical stability, iterative channel estimation is used in both network base stations and mobile UEs. Both solutions use spatial-temporal whitening for interference cancellation using the Whittle-Wiggins-Robinson Algorithm (WWRA). In many cases, the iterative channel estimation in UE is correlation based, as this method yields better performance in low C/I conditions. At high C/I, the correlation channel estimate is biased, which causes an irreducible bit error floor. Without error correction coding, data throughput will drop significantly due to increased packet retransmission. The iterative channel estimation in base stations is often least squared based, which yields better performance with high C/I. If C/I is low and the impairment is dominated by a few strong interferers (i.e., colored impairment), the least squares channel estimate performs much worse than the correlation channel estimate due to the distortion caused by the factor (SHS)−1 where S is the convolution matrix built from known training sequence symbols. Thus, neither technique is optimal under all conditions; however, wireless communication system receivers (whether deployed at the base station or in UE) must accurately receive data, and hence perform accurate channel estimation, under all channel conditions.